Time-series Crime Prediction Across the United States Based on Socioeconomic and Political Factors

TL;DR

This paper develops a deep learning model using socioeconomic and political data to predict crime trends across US states, aiming to improve prediction speed and accuracy for better resource allocation.

Contribution

It introduces a novel LSTM-GRU based model personalized with selected socioeconomic and political factors for crime prediction across states.

Findings

Model achieved an average total loss of 70.792.30

Average percent error of 9.74% indicating potential for improvement

Outliers significantly affected model performance

Abstract

Traditional crime prediction techniques are slow and inefficient when generating predictions as crime increases rapidly \cite{r15}. To enhance traditional crime prediction methods, a Long Short-Term Memory and Gated Recurrent Unit model was constructed using datasets involving gender ratios, high school graduation rates, political status, unemployment rates, and median income by state over multiple years. While there may be other crime prediction tools, personalizing the model with hand picked factors allows a unique gap for the project. Producing an effective model would allow policymakers to strategically allocate specific resources and legislation in geographic areas that are impacted by crime, contributing to the criminal justice field of research \cite{r2A}. The model has an average total loss value of 70.792.30, and a average percent error of 9.74 percent, however both of these values are impacted by extreme outliers and with the correct optimization may be corrected.